--- license: cc-by-4.0 dataset_info: - config_name: data-ms features: - name: file name dtype: string - name: IBSN dtype: string - name: subject dtype: string - name: topic dtype: string - name: Questions dtype: string - name: figures sequence: image - name: label sequence: string - name: Options dtype: string - name: Answers dtype: string splits: - name: eval num_bytes: 34663548 num_examples: 614 download_size: 34559856 dataset_size: 34663548 - config_name: data_en features: - name: FileName dtype: string - name: IBSN dtype: string - name: Subject dtype: string - name: Topic dtype: string - name: Questions dtype: string - name: Figures sequence: image - name: Label sequence: string - name: Options dtype: string - name: Answers dtype: string splits: - name: eval num_bytes: 34663548 num_examples: 614 download_size: 69119656 dataset_size: 69327096.0 tags: - mathematics - physics - llms - Malaysia - Asia size_categories: - n<1K configs: - config_name: data_en data_files: - split: eval path: data_en/train-* - config_name: data_ms data_files: - split: eval path: data_ms/train-* language: - en - ms --- # **A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects** This dataset provides a comprehensive evaluation set for tasks assessing reasoning skills in Science, Technology, Engineering, and Mathematics (STEM) subjects. It features questions in both English and Malay, catering to a diverse audience. **Key Features** * **Bilingual:** Questions are available in English and Malay, promoting accessibility for multilingual learners. * **Visually Rich:** Questions are accompanied by figures to enhance understanding and support visual and contextual reasoning. * **Focus on Reasoning:** The dataset emphasizes questions requiring logical reasoning and problem-solving skills, as opposed to simple recall of knowledge. * **Real-World Context:** Questions are derived from real-world scenarios, such as past SPM (Sijil Pelajaran Malaysia) examinations, making them relatable to students. **Dataset Structure** The dataset is comprised of two configurations: `data_en` (English) and `data_ms` (Malay). Both configurations share the same features and structure. **Data Fields** * **FileName:** Unique identifier for the source file (alphanumeric). * **IBSN:** International Standard Book Number of the source book (if available). * **Subject:** Academic subject (e.g., Physics, Mathematics). * **Topic:** Specific topic of the question within the subject (may be missing). * **Questions:** Main body of the question or problem statement. * **Figures:** List of associated image files related to the question (empty if no figures are present). * **Label:** Original caption or description of each image in the `imgs` list. * **Options:** Possible answer choices for the question, with keys (e.g., "A", "B", "C", "D") and corresponding text. * **Answers:** Correct answer to the question, represented by the key of the correct option (e.g., "C"). --- ## Data Instance Example ```json {     "FileName": "FC064244",     "ISBN": "9786294703681",     "Subject": "Physics",     "Topic": "Measurement",     "Questions": "State the physical quantity that can be measured using the measuring device shown in Diagram 1.",     "Figures": [         {             "label": "Diagram 1",             "path": "FC064244_C1_Q12_ImageFile_0.png"         }     ],     "Options": {         "A": "Weight",         "B": "Mass",         "C": "Amount of substance",         "D": "Volume"     },     "Answers": "B" } ``` **Data Split** The dataset is split between Physics and Mathematics subjects, with some questions lacking topic categorization. | Subject     | Instances with Topic | Instances without Topic | Total | |-------------|----------------------|-------------------------|-------| | Physics     | 316                  | 77                      | 393   | | Mathematics | 32                   | 189                     | 221   | **Known Limitations** * **Subject Coverage:** The current version focuses on Physics and Mathematics. Future releases will include more STEM subjects. * **Answer Accuracy:** Answers are extracted from various sources and may contain inaccuracies. **Source** The dataset is derived from a combination of resources, including: * SPM past-year exams * SPM mock exams * Educational exercise books **Data Acquisition Method** * Optical Character Recognition (OCR) for text extraction * Manual quality control (QC) to ensure data accuracy **Versioning and Maintenance** * **Current Version:** 1.0.0 * **Release Date:** December 27, 2024 * **Contact:** We welcome any feedback or corrections to improve the dataset quality. --- # License This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). --- # Getting Started You can access the dataset on Hugging Face using the following commands: ```bash # For English data pip install datasets from datasets import load_dataset dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_en") # For Malay data dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_ms") ``` --- # Bilingual STEM Dataset LLM Leaderboard This document summarizes the evaluation results for various language models based on **5-shot** and **First Token Accuracy**. The evaluation was conducted across four configurations: | **Model** | **en\_withfigures** | **en\_withoutfigures** | **ms\_withfigures** | **ms\_withoutfigures** | | --------------------------------- | ------------------- | ---------------------- | ------------------- | ---------------------- | | **gemini-2.0-flash-exp** | **63.70%** | 75.16% | **63.36%** | 75.47% | | **gemini-1.5-flash** | 49.66% | 67.39% | 50.00% | 64.28% | | **Qwen/Qwen2-VL-72B-Instruct** | 58.22% | 69.25% | 57.53% | 63.66% | | **gpt-4o** | 47.95% | 66.15% | 50.00% | 68.01% | | **gpt-4o-mini** | 41.10% | 55.90% | 38.36% | 52.80% | | **pixtral-large-2411** | 42.81% | 64.29% | 35.27% | 60.87% | | **pixtral-12b-2409** | 24.66% | 48.45% | 24.66% | 39.13% | | **DeepSeek-V3** | None | **79.19%** | None | **76.40%** | | **Qwen2.5-72B-Instruct** | None | 74.53% | None | 72.98% | | **Meta-Llama-3.3-70B-Instruct** | None | 67.08% | None | 58.07% | | **Llama-3.2-90B-Vision-Instruct** | None | 65.22% | None | 58.07% | | **sail/Sailor2-20B-Chat** | None | 66.46% | None | 61.68% | | **mallam-small** | None | 61.49% | None | 55.28% | | **mistral-large-latest** | None | 60.56% | None | 53.42% | | **google/gemma-2-27b-it** | None | 58.07% | None | 57.76% | | **SeaLLMs-v3-7B-Chat** | None | 50.93% | None | 45.96% | --- ## Notes In the repository, there is an `eval.py` script that can be used to run the evaluation for any other LLM. The evaluation results are based on the specific dataset and methodology employed. - The "First Token Accuracy" metric emphasizes the accuracy of predicting the initial token correctly. - Further analysis might be needed to determine the models' suitability for specific tasks. ### Attribution for Evaluation Code The `eval.py` script is based on work from the MMLU-Pro repository: - Repository: [TIGER-AI-Lab/MMLU-Pro](https://github.com/TIGER-AI-Lab/MMLU-Pro) - License: Apache License 2.0 (included in the `NOTICE` file) --- # **Contributors** - [**Gele**](https://huggingface.co/Geleliong) - [**Ken Boon**](https://huggingface.co/caibcai) - [**Wei Wen**](https://huggingface.co/WeiWen21)